Research Papers

Sensitivity of Input Epistemic Uncertainty on Nondeterministic Performance Estimates Using Nondeterministic Simulations

[+] Author and Article Information
Lawrence Hale

Kevin T. Crofton Department of Aerospace and
Ocean Engineering,
Virginia Tech,
Blacksburg, VA 24061

Mayuresh Patil

Associate Professor
Kevin T. Crofton Department of Aerospace and
Ocean Engineering,
Virginia Tech,
Blacksburg, VA 24061

Christopher J. Roy

Kevin T. Crofton Department of Aerospace and
Ocean Engineering,
Virginia Tech,
Blacksburg, VA 24061

Manuscript received January 12, 2017; final manuscript received May 30, 2017; published online June 20, 2017. Assoc. Editor: David Moorcroft.

J. Verif. Valid. Uncert 2(2), 021003 (Jun 20, 2017) (7 pages) Paper No: VVUQ-17-1001; doi: 10.1115/1.4037004 History: Received January 12, 2017; Revised May 30, 2017

This paper examines various sensitivity analysis methods which can be used to determine the relative importance of input epistemic uncertainties on the uncertainty quantified performance estimate. The results from such analyses would then indicate which input uncertainties would merit additional study. The following existing sensitivity analysis methods are examined and described: local sensitivity analysis by finite difference, scatter plot analysis, variance-based analysis, and p-box-based analysis. As none of these methods are ideally suited for analysis of dynamic systems with epistemic uncertainty, an alternate method is proposed. This method uses aspects of both local sensitivity analysis and p-box-based analysis to provide improved computational speed while removing dependence on the assumed nominal model parameters.

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Grahic Jump Location
Fig. 1

p-box representing aleatory and epistemic uncertainties

Grahic Jump Location
Fig. 2

Diagram illustrating p-box based analysis

Grahic Jump Location
Fig. 3

Simulated performance (3 m/s steady wind, severe turbulence) with sensor noise added to recorded simulation measurements



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